Effective long-term memory management is crucial for language models handling extended contexts. We introduce a novel framework that dynamically ranks memory entries based on relevance. Unlike previous works, our model introduces a novel relevance scoring and a pointwise re-ranking model for key-value embeddings, inspired by learning-to-rank techniques in information retrieval. Enhanced Ranked Memory Augmented Retrieval ERMAR achieves state-of-the-art results on standard benchmarks.

翻译：有效的长期记忆管理对于处理扩展上下文语言模型至关重要。我们提出了一种基于相关性动态排序记忆条目的新型框架。与先前工作不同，本模型受信息检索中排序学习技术启发，为键值嵌入引入了创新的相关性评分机制和逐点重排序模型。增强型排序记忆增强检索（ERMAR）在标准基准测试中取得了最先进的性能。